Towards Reliable Neural Generative Modeling of Detectors

TL;DR

This paper explores the use of GANs for simulating detector responses at the LHCb experiment, aiming to reduce computational costs while addressing systematic challenges in generative modeling.

Contribution

It introduces a GAN-based approach for detector simulation, highlighting systematic effects and potential pitfalls specific to high-energy physics applications.

Findings

GANs can effectively simulate LHCb detector responses

Systematic effects in GAN-based simulations are significant

Identifies challenges in applying GANs to physics simulations

Abstract

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.